Per-survivor based adaptive equalizer

ABSTRACT

A method used in an adaptive survivor based channel equalizer, the method comprises selecting at a decision time a survivor in a Viterbi trellis and a corresponding equalizer, adaptively updating at the decision time the corresponding equalizer to define a new corresponding equalizer for use at a next decision time, retrieving the new corresponding equalizer as defined at an earlier decision time, and using the new corresponding equalizer as defined at an earlier decision time as an equalizer for other survivors in the Viterbi trellis at the next decision time. A corresponding adaptive survivor based channel equalizer includes a fixed pre-filter configured to provide a pre-filtered signal to a reduced state sequence estimator (RSSE) which is configured for providing recovered symbols. A coefficient adaptor is coupled to the RSSE and configured to essentially perform the method.

RELATED APPLICATIONS

The present application is related to co-pending application Ser. No.11/726,318, titled ADAPTIVE EQUALIZER FOR COMMUNICATION CHANNELS byChen, which application is hereby incorporated in its entirety.

FIELD OF THE INVENTION

This invention relates in general to communication equipment andequalizers used in such equipment for equalizing or compensating signalsthat have been transmitted over communication channels and morespecifically to techniques and apparatus for improving per-survivorbased adaptive equalizers.

BACKGROUND OF THE INVENTION

Equalizers and adaptive equalizers are often used to compensate forinterference or distortions that occur in a signal during transmissionover a communication channel including anomalies in the signal that aregenerated at the transmitter or receiver and over the transmissionmedium. One common type of interference is generally referred to asInterSymbol Interference (ISI), which denotes the impact on a givensymbol that may result from neighboring symbols, normally previouslytransmitted symbols. ISI may result from various factors, includingintentional causes, e.g., transmitter filters used for spectralefficiency or receive filters used for adjacent channel interferencereduction, or undesirable and largely uncontrollable causes, e.g.,multi-path fading in the channel or transmitter & receiverimperfections.

Multi-path fading distorts a transmitted symbol in both shape as well assymbol duration or length (commonly referred to as dispersion).Communication channels may exist in differing fading environments withthe resultant distortion varying significantly. One example of a presentcommunications system that is widely used for various cellular phonecommunications systems is commonly referred to as EDGE (Enhanced Datarates for GSM Evolution, where GSM is an acronym for the Global Systemfor Mobile Communications). In EDGE signal transmissions, a transmittedsymbol pulse lasts for 4 symbol periods. It has been observed thatcommunication over a Hill Terrain (HT) channel or a Rural Area (RA)channel can expand or lengthen the received symbol pulse by 5.4 symbolperiods. Additionally, movement between the transmitter and receiver(typically due to a mobile station (MS) traveling relative to basestation (BS)) will result in changes in the received symbol pulse overtime, with the rate of change being a function of the rate of movement(speed of travel).

Many communication systems, such as an EDGE system utilize a trainingsequence that is transmitted as part of a burst (collection of amultiplicity of symbols) and in EDGE is located in the middle of theburst. The training sequence allows the receiver at the mobile or basestation to estimate the characteristics of the channel (channelestimation). Given the channel estimation various forms of Equalizershave been used or proposed which reduce the ISI. Unfortunately suchEqualizers tend to consume large amounts of processing resources, e.g.processor cycles, memory space, etc., and often have performancelimitations when dealing with different combinations of channelcomplexity (amount of or rates of fading) and signal levels.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying figures where like reference numerals refer toidentical or functionally similar elements throughout the separate viewsand which together with the detailed description below are incorporatedin and form part of the specification, serve to further illustratevarious embodiments and to explain various principles and advantages allin accordance with the present invention.

FIG. 1 depicts in a simplified and representative form, a high leveldiagram of a receiver including an adaptive equalizer in accordance withone or more embodiments;

FIG. 2 depicts a diagram of bit allocation in an exemplary EDGE bursttransmission;

FIG. 3 depicts a diagram of a portion of an exemplary Viterbi trelliswhich will serve to illustrate various processes in accordance with oneor more embodiments;

FIG. 4 depicts a representative high level block diagram of an adaptivechannel equalizer in accordance with one or more embodiments;

FIG. 5 depicts a representative high level block diagram of an adaptivechannel equalizer in accordance with one or more additional embodiments;and

FIG. 6 illustrates a flow chart of one detailed method embodiment ofupdating per survivor based equalizers, such as those in FIG. 4 and FIG.5 in accordance with one or more embodiments.

DETAILED DESCRIPTION

In overview, the present disclosure concerns communication equipment andequalizers used therein, e.g., adaptive survivor based equalizers withimproved performance and more efficient implementation, i.e., reductionin resources needed for such equalizers. More particularly variousinventive concepts and principles embodied in methods and apparatus foradaptive equalization will be discussed and disclosed.

The instant disclosure is provided to further explain in an enablingfashion the best modes, at the time of the application, of making andusing various embodiments in accordance with the present invention. Thedisclosure is further offered to enhance an understanding andappreciation for the inventive principles and advantages thereof, ratherthan to limit in any manner the invention. The invention is definedsolely by the appended claims including any amendments made during thependency of this application and all equivalents of those claims asissued.

It is further understood that the use of relational terms, if any, suchas first and second, top and bottom, and the like are used solely todistinguish one from another entity or action without necessarilyrequiring or implying any actual such relationship or order between suchentities or actions.

Much of the inventive functionality and many of the inventive principlesare best implemented with or in integrated circuits (ICs) includingdigital signal processors, possibly application specific ICs or ICs withintegrated processing controlled by embedded software or firmware. It isexpected that one of ordinary skill, notwithstanding possiblysignificant effort and many design choices motivated by, for example,available time, current technology, and economic considerations, whenguided by the concepts and principles disclosed herein will be readilycapable of generating such software instructions and programs and ICswith minimal experimentation. Therefore, in the interest of brevity andminimization of any risk of obscuring the principles and conceptsaccording to the present invention, further discussion of such softwareand ICs, if any, will be limited to the essentials with respect to theprinciples and concepts of the various embodiments.

Referring to FIG. 1, a simplified and representative high level diagramof a receiver including an adaptive equalizer in accordance with one ormore embodiments will be briefly discussed and described. FIG. 1, showsa receiver front end 101 that receives a transmitted signal, e.g., froman antenna, and then amplifies, filters, and converts or translates thatsignal to a lower frequency and normally also converts the resultantanalog signal to a digital signal, x_(n) at 102. The baseband signal,x_(n), from the receiver front end is coupled to a channel estimator 103or estimation function as well as a channel equalizer or equalizer 105.The output from the equalizer is provided as soft information (soft bitsor symbols, i.e. a symbol together with confidence information) to adecoder 107 that handles error correction, etc, and provides receivedbits or data that are coupled to further functions, e.g., media accesscontrol (MAC), etc. Other receivers may have other configurations inother embodiments.

The baseband signal, x_(n), received by the receiver can be representedas follows

$\begin{matrix}{x_{n} = {{\sum\limits_{i = 0}^{L - 1}{h_{i}I_{n - i}}} + \eta_{n}}} & (1)\end{matrix}$

where h_(n) is the Composite Channel Pulse Response (CPR) of L symbolperiods, including the effects of the transmitted symbol pulse,multi-path fading, and receiver filters; I_(n) is the informationsequence, and η_(n) represents the combination of Additive WhiteGaussian Noise (AWGN), co-channel interferences and adjacent channelinterferences. In, e.g., an EDGE signal transmission, the length of theCPR can be as long as 9.4 symbol periods in harsh fading, such as HillyTerrain (HT) and Rural Area (RA) as defined in 3GPP standards.

Referring additionally to FIG. 2, a diagram of symbol allocation in anexemplary EDGE burst transmission will be briefly discussed anddescribed and used to further describe the channel characterization andequalizer of FIG. 1 and others. FIG. 2 shows one burst in an EDGEsystem. It is understood that other systems and air interface standardsmay have other allocation maps. The EDGE burst occupies or has aduration of or spans 156.25 symbol periods 200. In EDGE, each burstduration is equivalent to 577 micro-seconds. As shown, the EDGE burstincludes and spans 3 tail symbols 201, a first data field 203 thatincludes or spans 58 symbols, a training sequence 205 of 26 symbols, asecond data field 207 that includes or spans 58 symbols, 3 more tailsymbols 209, followed by a gap 211 equivalent to 8.25 symbols times. Thetraining sequence is a predetermined sequence of symbols or bits asspecified in the relevant standards, e.g., EDGE standards. As is knownand specified in the various air interface standards for EDGE systems, 8bursts comprise one Time Division Multiplex Access (TDMA) frame, whichis transmitted via one radio frequency carrier. Different TDMA framesmay be frequency hopped on different radio frequency carriers.Twenty-six (26) TDMA frames comprises one multiframe. Fifty-onemultiframes are included in a superframe and 2048 superframes areincluded in a hyperframe.

The channel estimator 103 or estimation process estimates the compositeCPR h_(n) from the received signal x_(n) corresponding to the trainingsequence and the known training sequence I_(n) 205 for each transmittedand thus received burst, via the relationship in equation (1) using oneor more generally known techniques, e.g., Minimum Mean Square Error(MMSE) or Weighted MMSE. The equalizer 105, given the channel estimationfrom the channel estimator, generally operates or functions to remove orreduce distortion or interference in the received symbols or bits. Thetraining sequence by being placed in the middle of a burst allowsinformation symbols closest to the training sequence to facilitatereduction of the impact of channel variations over or during the burst.With this arrangement of the training sequence, the equalizer canoperate or adjust or compensate in both directions, i.e., from thetraining sequence to or across earlier received data or the first datafield 203 as well as from the training sequence to or across laterreceived data or the second data field 207, and thereby better accountfor channel variations over the time duration or span of the receivedsignal burst (or burst of the received signal).

One technique that may be used to recover the information sequence,I_(n), from the received or baseband signal, x_(n), as corrupted withISI and AWGN is known as Maximum Likelihood Sequence Estimation (MLSE)using a Viterbi Algorithm (VA). However, the complexity of VA increasesexponentially as the length of CPR h_(n) increases or modulation symbolset grows. For high order modulation, such as 8PSK in EDGE, and a CPRh_(n) that can exceed 9 taps, the complexity becomes impractical formost devices.

Referring to FIG. 3, a diagram of a portion of an exemplary Viterbitrellis which will serve to illustrate various processes in accordancewith one or more embodiments will be discussed and described. FIG. 3shows a portion of a Viterbi trellis that illustrates the VA. A fullViterbi trellis normally shows points in time, n 301, n−1 303, (twoshown) along a horizontal axis or dimension. These points in time may bereferred to or thought of as decision times and typically correspond tosymbol times for a given air interface or modulation scheme. The dots orsmall circles are representative of states, e.g., state 305, within theViterbi trellis and underlying system. A full trellis will have allpossible states at each decision time. The lines in FIG. 3 representstate transitions from one time to another time and are often referredto as branches. A state transition is the result of a system input,i.e., a symbol being transmitted and received. Thus a branch representsone or more symbols that would cause the transition from the state 306where the branch 307 originates at an earlier time, n−1 303 to the state305 where the branch 307 ends at the current time, n 301.

In particular the solid lines in FIG. 3 are surviving branches orsurvivors, e.g., surviving branch 307. A surviving path or survivor at agiven state and decision time n 301 is the combination of survivingbranches as traced back via the surviving branches, e.g., branch307+309, through the trellis. Generally the Viterbi Algorithm includesfor each state calculating a cumulative error for each branch arrivingat that state and selecting as the survivor at that state, the branchand hence survivor or surviving path with the best, e.g., lowest,cumulative error. Thus at state 305, time n, a cumulative error wasdetermined for each of branches 311 . . . 307 . . . 313 and based on thebest cumulative error, branch 307 was chosen as the survivor and anyother branches 311 . . . 313 were trimmed or dropped. At each statethere will be only one surviving branch entering that state; howeverthere may be more than one surviving branch leaving a state, at leasttemporarily. Because of the large number of states and branchesemanating from each state (during the application of the VA), the memoryrequired to remember the cumulative error at each state and resources toupdate these errors for each branch at typical symbol rates makes theutilization of MLSE via the VA impractical in many instances.

To reduce the complexity of MLSE, a Decision Feedback Equalizer (DFE)has been developed, in which, the CPR h_(n) is first converted into aCPR {b_(n), n=0, 1, 2, . . . , N_(b)} of minimum phase, or close tominimum phase, with b₀=1 by a pre-filter f′_(n), sometimes calledfeed-forward filter (see FIG. 4, 403; FIG. 5, 403), so that theinformation sequence I_(n) can be recovered in a decision directedfashion. In practice N_(b) is somewhat of a tradeoff between appropriateperformance and complexity. In some embodiments a value of 6 for N_(b)has been found to be appropriate. The process of calculating thecoefficients for the feed forward filter is known and may be referred toas DFE coefficient calculation. This may be or may be viewed asperformed as part of the channel estimator 103 functions.

In a DFE implementation, the received signal x_(n) is first filteredwith the feed forward filter or pre-filter f′_(n) 403 and the resultr_(n)=x_(n)*f′_(n) can be represented as follows

$\begin{matrix}{r_{n} = {I_{n} + {\sum\limits_{k = 1}^{N_{b}}{b_{l}I_{n - l}}} + w_{n}}} & (2)\end{matrix}$

Thus the information sequence I_(n) can be estimated in a decisiondirected fashion by

$\begin{matrix}{y_{n} = {r_{n} - {\sum\limits_{l = 1}^{N_{b}}{{\hat{I}}_{n - k}b_{l}}}}} & (3)\end{matrix}$

where y_(n) is the soft symbol and the Î_(n) is the hard symboldetermined from y_(n). The hard symbol estimate at symbol time n, Î_(n)is simply selected from the symbol alphabet as the symbol closest toy_(n). The DFE approach is essentially reflected in a simplified versionof FIG. 4 (see feed forward filter 403, combiner 411, hard decision 421,and feed back filter 431).

DFE has an error-propagation problem, which causes significantperformance degradation when the received signal x_(n) experiencesfading. This can be seen from (3) where an error in the symbol estimateat time n will impact results for N_(b) following symbols. To mitigatethe error propagation problem with DFE, the MLSE principle can beapplied to the pre-filter output signal r_(n). To reduce the complexityof conventional MLSE, Reduced State Sequence Estimator (RSSE) has beendeveloped that takes advantage of the minimum phase property of theremaining CPR b_(n) in the pr-filtered signal r_(n). These concepts arediscussed in, e.g.,] M. V. Eyuboglu, and S. U. H. Qureshi,“Reduced-State Sequence Estimation with Set Partitioning and DecisionFeedback,” IEEE Transactions on Communications, Vol. 36, No. 1, January1988, pp. 13-30 and P. R. Chevillat and E. Elefitheriou, “Decoding ofTrellis-Encoded Signal in the Presence of Inter-symbol Interference andNoise,” IEEE Trans. Communication, Vol. COM-37, pp. 669-676, July 1989.

RSSE can include two portions of state reduction. In the first portionof the state reduction, the ISI contribution due to the first few tapsof the CPR b_(n) is compensated by MLSE and the ISI contribution due tothe rest of the taps is compensated with DFE. The second portion of thestate reduction applies a technique called set partitioning for highorder modulation, such as 8PSK in EDGE, to the MLSE to further reducethe number of states. With set partitioning, a symbol alphabet isportioned into groups using an Ungerboeck principal. This principle isknown and discussed, e.g., in G. Ungerboeck, “Adaptive MaximumLikelihood Receive for Carrier Modulated Data-Transmission System,” IEEETrans. Communication, Vol. COM-22, pp. 624-636, May, 1974.

The search trellis with this set partitioning is built for the groups,rather than for the elements in the symbol alphabet and the trellissearch is essentially to determine which group the transmitted symbolsbelong to. To track channel changes over time due, e.g., to fading andRF front-end LO error, adaptive DFE, adaptive MLSE and per-survivorbased adaptive MLSE/RSSE have been developed. The per-survivor basedadaptive MLSE/DFE is discussed, e.g., in R. Raheli, A. Oplydoros andC-K. Tzou, “Per-Survivor Processing: A General Approach to MLSE inUncertain Environments,” IEEE Transactions on Communications, Vol. 43,No. 2/3/4, February/March/April 1995, pp. 354-364 and in Zhenhong Li, O.Piirainen, A. Mammela, “An Adaptive RSSE-PSP Receiver with a Pre-Filterfor EDGE System,” 2003, ICC '03. IEEE International Conference onCommunications, Vol. 5, 11-15 may 2003, pp. 3594-3598.

Adaptive DFE uses the difference between observation and the modelprediction based on the current CPR b_(n) and estimated informationsequence available so far to update the CPR b_(n) for the next step ofinformation sequence estimation. The difference can be represented asfollows:

$\begin{matrix}{e_{n} = {r_{n} - {\sum\limits_{l = 0}^{N_{b}}{b_{l}^{n - 1}{\hat{I}}_{n - l}}}}} & (4)\end{matrix}$

where b_(i) ^(n-1),n=0, 1, . . . , N_(b) is the current CPR; Î_(n),n=−∞,. . . , n is the estimated information sequence available so far; andr_(n) is the observation of the pre filter signal. In matrix form,Equation (4) can be written as

$\begin{matrix}{{e_{n} = {r_{n} - {{C^{\prime}(n)}{Y(n)}}}}{where}} & (5) \\{{C(n)} = {{\begin{bmatrix}b_{0}^{n - 1} \\\vdots \\b_{N_{b}}^{n - 1}\end{bmatrix}\mspace{11mu} {and}\mspace{14mu} {Y(n)}} = \begin{bmatrix}{\hat{I}}_{n} \\\vdots \\{\hat{I}}_{n - N_{b}}\end{bmatrix}}} & (6)\end{matrix}$

The CPR C(n) can be updated either by LMS approach or RLS approach. WithLMS approach, the CPR C(n) is updated as follows

C(n+1)=C(n)+βe _(n) Y*(n)  (7)

where β controls the tracking speed. With RLS approach, the CPR C(n) isupdated as follows

$\begin{matrix}{{\mu_{n} = {{Y^{\prime}(n)}{P( {n - 1} )}{Y^{*}(n)}}}{{K(n)} = {\frac{1}{\beta + {\mu (t)}}{P( {n - 1} )}{Y^{*}(n)}}}{{P(n)} = {\frac{1}{\beta}\lbrack {{P( {n - 1} )} - {{K(n)}{Y^{\prime}(n)}{P( {n - 1} )}}} \rbrack}}{{C(n)} = {{C( {n - 1} )} + {{K(n)}e_{n}}}}} & (8)\end{matrix}$

Where C(0) is initialized with the initial CPR b_(n) and P(0) isinitialized as a diagonal matrix of appropriate dimension. Note thatherein C(n), since it includes all coefficients for the adaptive filterof FIG. 4 may alternatively be referred to as an equalizer for acorresponding survivor.

It is shown that the CPR adaptation (7) or (8) relies on the estimatedinformation sequence Î_(n), thus the channel adaptation in DFE is adecision directed channel adaptation. The performance of this decisiondirected channel adaptation relies on the accuracy of the decision.Whenever there is an error in the decision of the information symbols,that error affects the next information symbol recovery, thus causingerror propagation.

To overcome the error propagation problem, adaptive MLSE andper-survivor based adaptive MLSE/RSSE have been proposed. The keydifference between adaptive MLSE and per-survivor based adaptiveMLSE/RSSE is in how the channel is adapted in conjunction with theViterbi search, instead of state reduction. Both of them use the samedecision directed channel adaptation. However, in adaptive MLSE, onechannel adaptation is made at each stage of trellis pruning for allsurvivor paths based a tentative data decision determined from thestrongest surviving path. In terms of channel adaptation, adaptive MLSEsimply provides a better estimated data sequence than DFE for thedecision directed channel adaptation, yet at the price of a decisiondelay that will cause degradation in the branch metric calculation inthe Viterbi trellis search. In per-survivor based adaptive MLSE/RSSE,however, each survivor has its own channel adaptation based on thetentative data decision over its own survivor. In per-survivor basedadaptive MLSE/RSSE, all survivors are in the trial, including the bestdata estimate, thus no decision delay is needed. However, per-survivorbased adaptive MLSE/RSSE requires N times of operations and memory forchannel adaptation where N is the number of states of the searchtrellis. From the discussion above, the number of states can be reducedusing Ungerboeck principals.

Referring to FIG. 4 a representative block diagram of an adaptivechannel equalizer in accordance with one or more embodiments will bediscussed and described. It will be observed that FIG. 4 shows in partan adaptive survivor or per-survivor based equalizer or MLSE/RSSEequalizer. This equalizer comprises a fixed pre-filter 403 configured tobe coupled to a receive signal x_(n) and provide a pre-filter orpre-filtered signal r_(n), where this pre-filter is configured viacoefficients (DFE coefficients) 405 which are provided from the channelestimator 103. Note that for each burst in an EDGE signal, these DFEcoefficients will normally be determined once for left hand and once forright hand movement through and away from the training sequence. Theadaptive channel equalizer is similar to equalizer 105 although varyingembodiments can include some of the functionality of the equalizer withthe channel estimator 103.

Further included in the equalizer of FIG. 4 is a reduced state sequenceestimator (RSSE) 407 coupled to the pre-filter signal and configured forproviding recovered symbols at 409. The RSSE 407 includes an adaptivefeedback filter 411 which is configured to extend each survivor in aViterbi trellis at a first time, e.g., n−1, to a second time, e.g., n,where the survivor and corresponding symbol estimates for that survivorare represented or provided by the per Survivor hard decision function413. FIG. 4 shows the combiner 415 comprising combiners 441, 442, 443,the hard decision function 413 comprising functions 421, 422, 423 andthe feedback filter 411 comprising feedback filters 431, 432, 433. Thissymbolizes the repetition of these functions for each survivor in acorresponding Viterbi trellis. In practice this may be 16, 32, 64 ormore states and corresponding survivors.

Additionally included is a coefficient adaptor 451 coupled to the RSSEand configured; to adaptively update first coefficients for the adaptivefeedback filter used for first equalizers associated with a firstportion of survivors at the first time, and to assign secondcoefficients for the adaptive feedback filter used for second equalizersassociated with a second portion of the survivors at the first time, thesecond coefficients being first coefficients for the adaptive feedbackfilter used for one or more of the first equalizers at an earlier timethat precedes the first time. At a given time or decision time n, thecoefficient adaptor selects a portion, i.e., one or more of allsurvivors, and updates the associated equalizers {C(n−1) becomes C(n)}for these survivors (provides new or updated coefficients for thefeedback filter) using equations (7) or (8) and the estimated symbolsÎ_(n), associated with the respective survivor. The coefficient adaptor451 selects one or more of the survivors where the correspondingequalizers have been updated and the equalizers for that one or moresurvivors from an earlier decision time, e.g., C(n−d) where d can be 1,2, 3, etc., and assigns this equalizer to a second portion of thesurvivors at time n. The recovered symbols are provided at 409 uponcompletion of the Viterbi trellis trimming where the details of doing soare not specifically shown since they are generally known.

The coefficient adaptor 451 is provided with an error e_(n) for eachsurvivor where this error term is determined in accordance with (4) andtogether with Î_(n), for each survivor used to update some equalizersC(n) for one or more survivors in accordance with (7) or (8). Note thatthe error term is the difference between the output y_(n) of thecombiner and the symbol estimates Î_(n) for or corresponding to eachsurvivor. As is known in a Viterbi trellis branch error metrics arecalculated for each branch arriving at a state and these branch errormetrics are added to a cumulative error metric that is associated withthe originating state to provide a new cumulative error metric for eachbranch arriving at the present state. Only the best or strongest branchis saved at a state and all other branches and corresponding paths aredropped or pruned or trimmed. The branch error metric can be themagnitude or square of the magnitude of the error e_(n). Thus thecoefficient adaptor 451 can also handle the Viterbi trellis trimming,etc. The coefficient adaptor 451 provides the updated equalizers, i.e.,coefficients b_(l) for the feedback filter or adaptive feedback filterwhere the coefficients are determined on a per-survivor basis (usingestimated symbols in accordance with the survivor).

One or more embodiments of the coefficient adaptor or equivalentfunctionality is configured to sort error metrics to provide a sortederror metrics list, where each error metric is associated with onesurvivor in the Viterbi trellis at the present decision or symbol time.The first equalizers can then be determined in accordance with thesorted error metrics list. In some embodiments, the coefficient adaptoris configured to select a strongest survivor based on a best errormetric (normally the smallest error metric) from the sorted errormetrics list and adaptively update first coefficients for the adaptivefeedback filter used for an equalizer associated with the strongestsurvivor. In some embodiments, the coefficient adaptor is configured toselect additional survivors (part of the first portion) based on thesorted error metrics list and adaptively update first coefficients forthe adaptive feedback filter used for additional equalizers associatedone for one with the additional survivors. In at least some instances,the coefficient adaptor is configured to choose the additional survivorsbased on the sorted error metrics list where the additional survivorshave associated error metrics, which are next best error metrics after astrongest survivor with a best error metric has been chosen. Thecoefficient adaptor can be configured to assign the second coefficientsfor the adaptive feedback filter used for the second equalizers whereinthe second coefficients are first coefficients at one decision timeearlier. In some embodiments, the coefficient adaptor will select theequalizer (corresponding coefficients) associated with the strongestsurvivor at d decision times earlier, where d=1, i.e., coefficientsassociated with C(n−1) for the strongest survivor and assign thesecoefficients and thus equalizer to all of the second portion of thesurvivors. Note that the second portion of the survivors may include allexcept the strongest survivor.

Referring to FIG. 5, a representative high level block diagram of anadaptive channel equalizer in accordance with one or more additionalembodiments will be discussed and described. FIG. 5 shows a novelstructure for a per survivor based adaptive equalizer. At a blockdiagram level FIG. 5 is similar to FIG. 4 with the addition of anadaptive feed forward filter 509 and a different adaptive feedbackfilter 511. The adaptive feedback filter 511 only adapts a portion, 1 .. . L_(b), of the N_(b) feedback filter coefficients as may be observedin FIG. 5. The adaptive channel equalizer of FIG. 5 is analogous to theequalizer 105.

It has been observed that different channel coefficients carry differentorder of channel variations. Channel adaptation on un-changed or lesschanged channel coefficients not only requires unnecessary complexityand calculations, but can also actually be harmful for performance.Because of this observation, the novel structure of data-aided channeladaptation shown in FIG. 5 was developed. This structure can be equallywell applied to adaptive DFE, adaptive MLSE, and per-survivor basedadaptive MLSE/RSSE. The difference between the conventional data-aidedchannel adaptation and the novel structure of data-aided channeladaptation is in the structure of the adaptation. In the novel channeladaptation structure, only one or a few more channel coefficients areadapted, and the rest of the channel coefficients are not adapted. Theerror calculation in the general form of the novel structure can begiven as follows:

$\begin{matrix}{{e_{n} = {( {I_{n} + {\sum\limits_{i = {L_{b} + 1}}^{N_{b}}{I_{n - l}b_{l}}}} ) - {{C^{\prime}(n)}{Y(n)}}}}{where}} & (9) \\{{C(n)} = {{\begin{bmatrix}f_{K_{1}}^{({n - 1})} \\\vdots \\f_{0}^{({n - 1})} \\f_{1}^{({n - 1})} \\\vdots \\f_{K_{2}}^{({n - 1})} \\b_{1}^{({n - 1})} \\\vdots \\b_{L_{b}}^{({n - 1})}\end{bmatrix}\mspace{11mu} {and}\mspace{14mu} {Y(n)}} = \begin{bmatrix}r_{n - K_{1}} \\\vdots \\r_{n} \\r_{n + 1} \\\vdots \\r_{n + K_{2}} \\I_{n - 1} \\\vdots \\I_{n - L_{b}}\end{bmatrix}}} & (10)\end{matrix}$

and adaptation of C(n) can be performed using (7) or (8) above. It isnoted that C(n) includes all of the coefficients that define theadaptive filters shown in FIG. 5. Thus C(n) may be referred to or viewedas the equalizer associated with a given survivor in the discussionsbelow.

The simplest case of the novel structure is when K₁=0, K₂=0, andL_(b)=0, in which only one coefficient f₀ ^(n-1) in the adaptive feedforward filter is adapted. In this instance the matrix calculation in(7) and (8) are reduced to scalar calculations, resulting in dramaticcomplexity reduction. However, it is noticed that the simplest caseprovides dramatic performance gain in complex and fast fading channelsspecified in 3GPP relative to known approaches. Meanwhile the simplestcase does not cause degradation in slow fading and static conditions andcauses slight degradation only in complex and fast fading relative tothose with optimal choice of K₁, K₂, and L_(b). An extended discussionregarding the selection of these parameters is provided in co-pendingapplication Ser. No. 11/726,318 which was referred to above and has beenincluded herein by reference.

As described with reference to FIG. 4 and as will become apparent forFIG. 5, a further complexity reduction for the per-survivor basedadaptive RSSE with negligible performance compromise even in complex andfast fading environments is available with the principles discussed anddescribed herein. The complexity reduction is achieved by reducing thenumber of equalizers to be adapted, yet still using the per-survivoradaptation principle. The number of equalizers to be adapted in eachstage of trellis pruning can be as low as one with the new structure ofdata-aided channel adaptation.

In conventional adaptive MLSE/RSSE, at each stage of trellis pruning ortrimming, the strongest survivor is determined, and the sequence up tothe current stage on the strongest survivor is thus determined. This maybe called a tentative data sequence denoted as Î_(n), n=−∞, . . . , n−1,n, where n represents the current stage of pruning. A portion of thetentative data sequence, Î_(n), n=−∞, . . . , n−d, and the receivedsignal r_(n-d) are then used to update the channel, resulting in updatedchannel b_(k) ^(n-d), k=0, 1, 2, . . . , N_(b), where d is the decisiondelay. The decision delay d is critical for the adaptive MLSE/RSSE to besuccessful since the strongest surviving path is a premature solution atthe current stage of trellis pruning. The updated equalizer b_(k)^(n-d), k=0, 1, 2, . . . , N_(b) will be applied to the next step oftrellis pruning at stage n+1. Thus even if the tentative data sequenceis correct, and the adapted channel is correct at time n−d, it may notbe correct for the update at time n+1. For slow fading, the performancedegradation due to this time delay may not be significant. However, forfast fading the performance impact can be significant since the updatedchannel b_(k) ^(n-d), k=0, 1, 2, . . . , N_(b) will be applied to branchmetric calculations at d symbols late in the Viterbi trellis search. Tomitigate the impact by reducing the delay d will lead to using prematuretentative data sequence for the channel adaptation. Therefore, adaptiveMLSE/RSSE has a fundamental limitation for fast fading channels.Per-survivor based adaptive MLSE/RSSE will remove or mitigate thisfundamental limitation.

To overcome the problems associated with the adaptive MLSE/RSSE,per-survivor based adaptive MLSE/RSSE has been proposed, in which thedata sequence associated with each survivor is used for the data-aidedchannel adaptation. Therefore, for an N-state MLSE/RSSE, N sets ofequalizers are updated, one for each survivor. The updated channelassociated with a survivor is used for the extension of that survivor.The rational behind the per-survivor based adaptation is that wheneverincomplete knowledge of the channel prevents us from accuratelycalculating an error or transition metric in trellis pruning, thechannel on each survivor is updated based on the data sequenceassociated with the survivor leading to that transition. Since one ofthe survivors is correct, the channel adaptation associated with thatsurvivor is made using the correct data sequence. In other words, thebest survivor is extended using the best data sequence availableregardless of our temporary ignorance as to which survivor is the best.

The challenge with the per-survivor based adaptive MLSE/RSSE is that Nequalizers are adapted independently at each stage of trellis pruningfor an N-state MLSE/RSSE. For a 64-state RSSE, a set of 64 equalizersare updated at each stage of trellis pruning, and memory required is thespace to store 64 equalizers and associated update information. Thusper-survivor based adaptive MLSE/RSSE encounter big challenges in manyapplications, especially in handhold mobile devices. An efficientsolution with negligible performance compromise is highly desirable formany practical applications.

Returning to FIG. 5, a receive signal x_(n) is coupled to the feedforward (fixed, non adaptive) filter 403 which is configured with DFEcoefficients from the channel estimator at 405 and which provides apre-filtered signal r_(n) that is coupled to an RSSE 507 (via adaptivefeed forward filter 509) and which is configured to provide recoveredsymbols at 409 and which includes an adaptive feedback filter 511 (shownas per survivor adaptive feedback filters 531, 532, 533). The adaptivefeedback filter 511 is configured and operable to extend each survivorin a Viterbi trellis (corresponding symbols) at a first time to a secondand subsequent time. The RSSE 507 is intercoupled to a coefficientadaptor 551 and provides error metrics e_(n) corresponding to eachbranch or at least each surviving branch within the Viterbi trellis. Thecoefficient adaptor 551 is initialized for each half burst by the DFEcoefficients, etc. from the channel estimator at 405 and is configuredto provide b_(n) coefficients for the adaptive feedback filter 511 andalso handles adaptation of these coefficients for l=1, 2, . . . , L_(b)and the adaptation of f_(k) feed forward filter coefficients.

More specifically the pre-filtered signal is coupled to an adaptive feedforward filter 509, shown as adaptive filters 517, 518, 519 (symbolic ofan adaptive feed forward filter for each survivor in a Viterbi trellis).The adaptive feed forward filter is configured to compensate thepre-filter signal and couple the pre-filter signal as compensated to theRSSE. The adaptive feed forward filter 509 provides a signal r_(nc),which is the pre filtered signal compensated, e.g., for amplitude andphase variations due largely to channel fading, to a combiner 515 (shownas combiners 541, 542, 543). The combiner 515 combines the pre-filteredsignal as compensated and the output signals from the adaptive feed backfilter 511 to provide a respective y_(n) for each survivor in a Viterbitrellis. The per-survivor estimated symbols or symbol sequence Î_(n) isprovided by a decision function 513 (shown as functions 521, 522, 523)to the adaptive feedback filter 511.

Similar to FIG. 4, an error e_(n) and Î_(n) is provided for eachsurvivor to a coefficient adaptor 551. The error e_(n) is determinedfrom equation (9) and (10) and is utilized together with Î_(n) and r_(n)by the coefficient adaptor to adaptively update, via equation (7) or(8), first coefficients for the adaptive feedback filter used for firstequalizers associated with a first portion of survivors at the firsttime, and to assign second coefficients for the adaptive feedback filterused for second equalizers associated with a second portion of thesurvivors at the first time, the second coefficients being firstcoefficients for the adaptive feedback filter used for one or more ofthe first equalizers at an earlier time that precedes the first time.The coefficient adaptor 551 has similar functionality to that discussedabove with reference to FIG. 4 and coefficient adaptor 451; however, forthe survivors among the first portion, fewer of the adaptive feedbackfilter coefficients are updated and the adaptor also updates one or morecoefficients f_(k) for the adaptive feed forward filter 509. Generallyfor the adaptive feed back filter 511 only the first L_(b) coefficientsare updated, where in some embodiments, L_(b) can be as small as 0(meaning no adaptation for feed back filter), with the remainingcoefficients, L_(b)+1, . . . , N_(b), remain set to their initial valueas provided from the channel estimator.

In one or more embodiments, the coefficient adaptor is furtherconfigured to adaptively update first filter coefficients for theadaptive feed forward filter 509 used for the first equalizersassociated with the first portion of survivors at the first time and toassign second filter coefficients for the adaptive feed forward filterused for the second equalizers associated with a second portion of thesurvivors at the first time, where the second filter coefficients can befirst filter coefficients for the adaptive feed forward filter used forone or more of the first equalizers at the earlier time. Similar to theFIG. 4, the coefficient adaptor 551 is configured to sort error metricsto provide a sorted error metrics list, where each error metric isassociated with one survivor in the Viterbi trellis, and where the firstequalizers are determined in accordance with the sorted error metricslist. The coefficient adaptor can be configured to select a strongestsurvivor based on a best error metric from the sorted error metrics list(normally survivor with the smallest error or cumulative error metric)and adaptively update first coefficients for the adaptive feedbackfilter and adaptively update first filter coefficients for the adaptivefeed forward filter, where both filters are used for an equalizerassociated with the strongest survivor. In addition to the strongestsurvivor, the coefficient adaptor can be configured to select additionalsurvivors based on the sorted error metrics list (next best errormetrics or the like) and adaptively update first coefficients for theadaptive feedback filter and adaptively update first filter coefficientsfor the adaptive feed forward filter, both filters used for additionalequalizers associated one for one with the additional survivors.

It will be evident from the above and further elaborated on below that adramatic reduction in the memory and computational resources associatedwith the per-survivor based adaptive MLSE/RSSE is provided by theequalizers of FIG. 4 and FIG. 5. As generally noted above, theseequalizers only update N_(track) equalizers on N_(track) most likelysurvivors (strongest and next strongest, etc survivors), where N_(track)can be as small as 1. For the remainder of the survivors, theircorresponding equalizers are not adaptively updated (equation (7) and(8) are not calculated for the remaining equalizers); but ratherassigned the equalizer from the one or more of the strongest survivorsat d symbols earlier as determined at the current stage n, where d canbe as small as 1. N_(track) determines the number of equalizers to beupdated in each stage of trellis pruning; and d determines the memoryrequirement to save (d+1)N_(track) equalizers. For 8PSK in EDGE usingthe data-aided channel adaptation of the new structure (9) and (10), itis found that N_(track) can be as small as 1, and d can be as small as1, which means only one equalizer needs to be updated in each stage oftrellis pruning and memory required is the storage to store twoequalizers and associated adaptive information {C(n), P(n), C(n−1),P(n−1)}.

Referring to FIG. 6, a flow chart of one detailed method embodiment ofupdating per survivor based equalizers, such as those in FIG. 4 or FIG.5 in accordance with one or more embodiments will be discussed,described and generalized upon. Generally the method begins and showstrellis pruning 501 taking place. As noted above all branches that endat a state for a decision or symbol time n, are compared in terms of anassociated cumulative error metric and only the strongest of thesebranches and their associated surviving path is saved with all otherbranches and their associated paths being trimmed, or pruned, ordropped.

The method of FIG. 6 is used in an adaptive survivor based channelequalizer and generally includes selecting at a decision time n, asurvivor in a Viterbi trellis 501, 503, where the survivor has acorresponding equalizer C(n−1), i.e., as defined at an immediatelyearlier decision time, adaptively updating at the decision time n thecorresponding equalizer (and possibly other equalizers) 509, 511 todefine the corresponding equalizer C(n) for use at a next decision time;retrieving the corresponding equalizer as defined at an earlier decisiontime C(n−d) 505; and using the corresponding equalizer as defined at anearlier decision time as an equalizer 507 for other survivors in theViterbi trellis at the next decision time.

More specifically, assume we just finished trellis pruning for a64-state RSSE at symbol index n, resulting in 64 survivors, and 64corresponding cumulative path error metrics M_(cm) ^(n)(l), l=1, 2, . .. , 64. Note that the same or similar process is used for a 16 or 32state RSSE. The adaptation for these 64 equalizers under theper-survivor processing principle is made as follows. Sort the 64cumulative metrics 503 in ascending order, resulting in B_(l), l=1, 2, .. . , 64, which holds the indices of the 64 survivors in an ascendingorder in terms of cumulative error metric saved in M_(cm) ^(n)(l), l=1,2, . . . , 64. B₁ is the index of the smallest cumulative path errormetric, thus the index of the strongest survivor of the 64 survivors.B_(l), l=1, 2, . . . , N_(track) are the indices of the N_(track)strongest survivors. Identify the strongest survivor B_(l), and identifyequalizer C(n−d,B_(l)), the equalizer on survivor B₁ at d symbolsearlier 505. Identify survivors B_(l)l=N_(track)+1, . . . , 64, i.e.,all except N_(track) strongest survivors, and update their equalizerswith the equalizer C(n−d,B₁) 507.

Identify the strongest survivor B_(l) starting with l=1, the Nb latestsymbols on the survivor B_(l), and the current equalizer C(n−1, B_(l))associated with the survivor B_(l), where C(n−1, B_(l)) was used toprovide e_(n) for the latest branch error metric and thus presentcumulative error metric for that survivor B_(l) 509. Update theequalizer for survivor B_(l) with the Nb latest symbols, latest errore_(n) and the current equalizer C(n−1, B_(l)) to provide C(n, B_(l))using equations (5), (6) and (7) or (8) or equations (9), (10), and (7)or (8) 511 and repeat 509, 511 for other strongest survivors until1=N_(TRACK) 513. Generally 509, 511, 513 result in updating N_(track)equalizers on the N_(track) strongest survivors corresponding to indicesB_(l), l=1, 2, . . . , N_(track). For each of the N_(track) equalizeradaptations, first search back on the survivor path to identify theN_(b) latest symbols on the survivor path, where N_(b) is the number ofthe feedback taps in the feed back filter for the equalizer. The N_(b)latest symbols are then used to update the adaptive coefficients givenin the vector C(n) with the appropriate equations.

Although not specifically shown the N_(track)+1 equalizers and updateinformation P(n) will need to be saved in memory, where N_(track)equalizers are just updated, and 1 equalizer is the one inherited C(n−d,B1). The method of FIG. 6 can be repeated as needed for additionaldecision times n+1, n+2, etc. and can be implemented is a suitable DSPor other integrated circuit with appropriate processing resources.

The flow chart of FIG. 6 in general terms illustrates a method thatincludes sorting or rank ordering error metrics, where each error metricis associated with one survivor in a Viterbi trellis, to provide asorted error metrics list and then selecting a survivor, i.e., strongestsurvivor or the like in accordance with the sorted error metrics list.The survivor that is selected will typically be the strongest survivoror survivor with a best error metric based on the sorted error metricslist. The method also illustrates selecting or choosing one or moreadditional survivors in accordance with the sorted error metrics listand adaptively updating at the decision time the additional equalizers,where the additional equalizers correspond one to one to the additionalsurvivors. The one or more additional survivors are typically selectedas those one or more additional survivors that have associated errormetrics, which are next best error metrics after a strongest survivorwith a best error metric has been selected. Furthermore retrieving thenew corresponding equalizer as defined at the earlier decision time cancomprises retrieving the new corresponding equalizer as defined at thedecision time C(n−1, B1). As noted above the Viterbi trellis can be a64, 32, 16, etc. state trellis and the other survivors can be allsurvivors other than the strongest survivor. Updating or adaptivelyupdating at the decision time the corresponding equalizer furthercomprises updating feed forward filter coefficients for FIG. 5embodiments and feedback filter coefficients for FIG. 4 and FIG. 5embodiments based on the symbols corresponding to the survivor, e.g.,symbols as traced back along the survivor.

The rational behind this efficient solution is as follows. In theViterbi trellis search for a MLSE/RSSE with large search trellis, at theend of each stage of trellis pruning, not all survivors have the samelikelihood to lead to the final survivor at the next stage of trellispruning. The cumulative metrics of these survivors contain someinformation regarding which ones of them are more likely than others. Inthe efficient solution, the N_(track) equalizers on the N_(track) mostlikely survivors are updated with the per-survivor processing principle(using symbols from that survivor), and the equalizers on the restsurvivors are updated with the best of the possible equalizers withslight delay (d symbols). If none of the survivors outside the N_(track)most likely survivors is elevated to the N_(track) most likely survivorsat the end of next stage of trellis pruning, no performance degradationshould be expected. If one of the survivors outside the N_(track) mostlikely survivors is elevated to one of the N_(track) most likelysurvivors in next stage of the trellis pruning, the impact of the delayassociated with that equalizer in current stage will be removed duringthe coefficient adaptation at the end of next stage of trellis pruning.Therefore, the impact of decision delay associated with this efficientsolution will not be accumulated. Thus, no performance degradation isobserved with this efficient per-survivor based MLSE/RSSE in moderatefading speed such as in TU50 and HT50 and only slight degradation isobserved in fast fading such as RA250. The degradation in RA250 is about1 dB relative to full scale of per-survivor based RSSE, which gains morethan 10 dB with the new adaptive RSSE structure of FIG. 5.

This disclosure is intended to explain how to fashion and use variousembodiments in accordance with the invention rather than to limit thetrue, intended, and fair scope and spirit thereof. The foregoingdescription is not intended to be exhaustive or to limit the inventionto the precise form disclosed. Modifications or variations are possiblein light of the above teachings. The embodiment(s) was chosen anddescribed to provide the best illustration of the principles of theinvention and its practical application, and to enable one of ordinaryskill in the art to utilize the invention in various embodiments andwith various modifications as are suited to the particular usecontemplated. All such modifications and variations are within the scopeof the invention as determined by the appended claims, as may be amendedduring the pendency of this application for patent, and all equivalentsthereof, when interpreted in accordance with the breadth to which theyare fairly, legally, and equitably entitled.

1. A method used in an adaptive survivor based channel equalizer, themethod comprising: selecting at a decision time a survivor in a Viterbitrellis, the survivor having a corresponding equalizer defined at animmediately earlier decision time; adaptively updating at the decisiontime the corresponding equalizer to define the corresponding equalizerfor use at a next decision time; retrieving the corresponding equalizeras defined at an earlier decision time; and using the correspondingequalizer as defined at an earlier decision time as an equalizer forother survivors in the Viterbi trellis at the next decision time.
 2. Themethod of claim 1 further comprising sorting error metrics, each errormetric associated with one survivor in the Viterbi trellis, to provide asorted error metrics list and wherein the selecting the survivorcomprises selecting the survivor in accordance with the sorted errormetrics list.
 3. The method of claim 2 wherein the selecting thesurvivor further comprises selecting a strongest survivor with a besterror metric based on the sorted error metrics list.
 4. The method ofclaim 2 further comprising choosing one or more additional survivors inaccordance with the sorted error metrics list and adaptively updating atthe decision time one or more additional equalizers, the one or moreadditional equalizers corresponding one to one to the one or moreadditional survivors.
 5. The method of claim 4 wherein the choosing theone or more additional survivors in accordance with the sorted errormetrics list further comprises selecting one or more additionalsurvivors that have associated error metrics, which are next best errormetrics after a strongest survivor with a best error metric has beenselected.
 6. The method of claim 1 wherein the retrieving thecorresponding equalizer as defined at the earlier decision time furthercomprises retrieving the corresponding equalizer as defined at theimmediate earlier decision time.
 7. The method of claim 1 wherein theViterbi trellis is a 64 state trellis and the other survivors comprise63 survivors.
 8. The method of claim 1 wherein the Viterbi trellis is a32 state trellis and the other survivors comprise 31 survivors.
 9. Themethod of claim 1 wherein the Viterbi trellis is a 16 state trellis andthe other survivors comprise 15 survivors.
 10. The method of claim 1wherein the adaptively updating at the decision time the correspondingequalizer further comprises updating feed forward filter coefficientsand feedback filter coefficients based on the symbols corresponding tothe survivor.
 11. An adaptive survivor based channel equalizercomprising: a fixed pre-filter configured to be coupled to a receivesignal and provide a pre-filter signal; a reduced state sequenceestimator (RSSE) coupled to the pre-filter signal and configured forproviding recovered symbols, the RSSE including an adaptive feedbackfilter configured to extend each survivor in a Viterbi trellis at afirst time to a second time; and a coefficient adaptor coupled to theRSSE and configured: to adaptively update at the first time firstcoefficients for the adaptive feedback filter used for first one or moreequalizers associated with a first portion of survivors, and to assignat the first time second coefficients for the adaptive feedback filterused for second equalizers associated with a second portion of thesurvivors, the second coefficients being first coefficients for theadaptive feedback filter used for one or more of the first one or moreequalizers at an earlier time.
 12. The adaptive survivor based channelequalizer of claim 11 wherein the coefficient adaptor is configured tosort error metrics to provide a sorted error metrics list, each errormetric associated with one survivor in the Viterbi trellis, the firstone or more equalizers determined in accordance with the sorted errormetrics list.
 13. The adaptive survivor based channel equalizer of claim12 wherein the coefficient adaptor is configured to select a strongestsurvivor based on a best error metric from the sorted error metrics listand adaptively update first coefficients for the adaptive feedbackfilter used for an equalizer associated with the strongest survivor. 14.The adaptive survivor based channel equalizer of claim 13 wherein thecoefficient adaptor is configured to select additional survivors basedon the sorted error metrics list and adaptively update firstcoefficients for the adaptive feedback filter used for additionalequalizers associated one for one with the additional survivors.
 15. Theadaptive survivor based channel equalizer of claim 14 wherein thecoefficient adaptor is configured to choose the additional survivorsbased on the sorted error metrics list where the additional survivorshave associated error metrics, which are next best error metrics after astrongest survivor with a best error metric has been chosen.
 16. Theadaptive survivor based channel equalizer of claim 11 wherein thecoefficient adaptor is configured to assign the second coefficients forthe adaptive feedback filter used for the second equalizers wherein thesecond coefficients are first coefficients at one decision time earlier.17. The adaptive survivor based channel equalizer of claim 11 furthercomprising an adaptive feed forward filter configured to compensate thepre-filter signal and couple the pre-filter signal as compensated to theRSSE and wherein the coefficient adaptor provides filter coefficients tothe adaptive feed forward filter.
 18. The adaptive survivor basedchannel equalizer of claim 17 wherein the coefficient adaptor is furtherconfigured: to adaptively update at the first time first filtercoefficients for the adaptive feed forward filter used for the first oneor more equalizers associated with the first portion of survivors; andto assign at the first time second filter coefficients for the adaptivefeed forward filter used for the second equalizers associated with asecond portion of the survivors, the second filter coefficients beingfirst filter coefficients for the adaptive feed forward filter used forone or more of the first one or more equalizers at the earlier time. 19.The adaptive survivor based channel equalizer of claim 18 wherein thecoefficient adaptor is configured to sort error metrics to provide asorted error metrics list, each error metric associated with onesurvivor in the Viterbi trellis, the first one or more equalizersdetermined in accordance with the sorted error metrics list.
 20. Theadaptive survivor based channel equalizer of claim 19 wherein thecoefficient adaptor is configured to select a strongest survivor basedon a best error metric from the sorted error metrics list and adaptivelyupdate first coefficients for the adaptive feedback filter andadaptively update first filter coefficients for the adaptive feedforward filter, both filters used for an equalizer associated with thestrongest survivor.
 21. The adaptive survivor based channel equalizer ofclaim 19 wherein the coefficient adaptor is configured to selectadditional survivors based on the sorted error metrics list andadaptively update first coefficients for the adaptive feedback filterand adaptively update first filter coefficients for the adaptive feedforward filter, both filters used for additional equalizers associatedone for one with the additional survivors.
 22. The adaptive survivorbased channel equalizer of claim 11 wherein the coefficient adaptorconfigured to adaptively update at the first time the first coefficientsis further configured to adaptively update the first coefficients bycalculating new first coefficients based on previous coefficients and anerror.